Bayesian analysis of autoregressive moving average processes with unknown orders
نویسنده
چکیده
A Bayesian model selection for modelling a time series by an autoregressive–moving–average model (ARMA) is presented. The posterior distribution of unknown parameters and the selected orders are obtained by the Markov chain Monte Carlo (MCMC) method. An MCMC algorithm that represents the parameters of the model as a point process has been implemented. The method is illustrated on simulated series and a real dataset. © 2006 Elsevier B.V. All rights reserved.
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ورودعنوان ژورنال:
- Computational Statistics & Data Analysis
دوره 51 شماره
صفحات -
تاریخ انتشار 2006